The birth of artificial intelligence (1956)
McCarthy convinced Minsky, Claude Shannon, and Nathaniel Rochester to help him bring
together U.S. researchers interested in automata theory, neural nets, and the study of
intelligence. They organized a two-month workshop at Dartmouth in the summer of 1956.
Perhaps the longest-lasting thing to come out of the workshop was an agreement to adopt
McCarthy's new name for the field: artificial intelligence.
Early enthusiasm, great expectations (1952-1969)
The early years of Artificial Intelligence were full of successes-in a limited way. General
Problem Solver (GPS) was a computer program created in 1957 by Herbert Simon and Allen
Newell to build a universal problem solver machine. The order in which the program
considered sub goals and possible actions was similar to that in which humans approached the
same problems. Thus, GPS was probably the first program to embody the "thinking humanly"
approach.
IBM, Nathaniel Rochester and his colleagues produced some of the first AI pro-grams.
Herbert Gelernter (1959) constructed the Geometry Theorem Prover, which was able to prove
theorems that many students of mathematics would find quite tricky.
Lisp was invented by John McCarthy in 1958 while he was at the Massachusetts Institute
of Technology (MIT). In 1963, McCarthy started the AI lab at Stanford.
Knowledge-based systems: The key to power? (1969-1979)
Dendral was an influential pioneer project in artificial intelligence (AI) of the 1960s, and the
computer software expert system that it produced. Its primary aim was to help organic chemists
in identifying unknown organic molecules, by analyzing their mass spectra and using
knowledge of chemistry. It was done at Stanford University by Edward Feigenbaum, Bruce
Buchanan, Joshua Lederberg, and Carl Djerassi.
AI becomes an industry (1980-present)
In 1981, the Japanese announced the "Fifth Generation" project, a 10-year plan to build
intelligent computers running Prolog. Overall, the AI industry boomed from a few million
dollars in 1980 to billions of dollars in 1988.
AI becomes a science (1987-present)
In recent years, approaches based on hidden Markov models (HMMs) have come to dominate
the area.
Speech technology and the related field of handwritten character recognition are already
making the transition to widespread industrial and consumer applications.
The Bayesian network formalism was invented to allow efficient representation of, and rigorous
reasoning with, uncertain knowledge.
The emergence of intelligent agents (1995-present)
One of the most important environments for intelligent agents is the Internet.
Components of Artificial Intelligence:
Reasoning, problem-solving: Researchers had developed machines with algorithms that
enable machines to solve puzzles or quiz similar to humans. AI can also deal with uncertain or
incomplete information through advanced algorithms.
Knowledge Representation: It is the representation of all the knowledge which is stored by an
agent to make an expert system. Knowledge can be a set of objects, relations, concepts, or
properties.
Planning: Intelligent agents should be able to set goals and make plans to achieve those goals.
They should be able to visualize the future and make predictions about their actions taken for